Review of role of machine learning models in coronary heart disease detection accuracy (Record no. 17260)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | a |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220805152552.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220805b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 12076 |
| Author | Agarwal, Tarun |
| 245 ## - TITLE STATEMENT | |
| Title | Review of role of machine learning models in coronary heart disease detection accuracy |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.7 (01), Jan-Feb. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | New Delhi |
| Name of publisher, distributor, etc. | Associated Management Consultants |
| Year | 2022 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 36-44p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | <br/>Abstract<br/><br/>According to the World Health Organization, heart disease is the most widespread disease in the world, affecting over a billion people. Generally, the lifestyles of people are occasionally plagued by stress, worry, and sadness, among other things. The early detection of this condition is tough, and it is a difficult task in medical science. The goal of this research is to better understand the detection accuracy of particular machine learning models (MLMs), as well as their limitations and categorization strategies. Many researchers used classification techniques such as Naive Bays (NB), decision trees (DT), Cooperative Neural-Network Ensembles (CNNEs), logistic regression (LR), Support Vector Machine (SVM), Least Square Twin Support Vector Machine (LS-SVM), k-Nearest Neighbor (KNN), Bays Net (BN), Artificial Neural Network (ANN), and Multi-Layer Perception (MLP) (MLP). In total, the dataset contains more than 50 features attributes. To boost accuracy, the study uses different feature selection approaches to identify the most appropriate features for detecting the disease. The present study achieved a maximum classification accuracy of 96.29%, and there is a need to improve accuracy in the shortest period possible by developing single MLMs for detecting and selecting specific features. Many studies employ hybrid approaches to improve the accuracy of percentages by layering two or more classification algorithms (based on specified symptoms and traits of a human being). It is not always more efficient and time-consuming. As a result, flexible MLMs with feature selection and reduction strategies are required. Further, the current research focuses on boosting accuracy and includes future viewpoints or uses of research as well.<br/><br/>Keywords<br/><br/>Artificial Neural Network, Cooperative Neural-Network Ensembles, K-Nearest Neighbor, Least Square Twin Support Vector Machine, Multi-Layer Perception, Naive Bays, Support Vector Machine. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4622 |
| Topical term or geographic name entry element | Computer Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 17470 |
| Co-Author | Sharma, Hemant |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 17471 |
| Co-Author | Latha, Challa Madhavi |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 17472 |
| Co-Author | Gupta, Sitaram |
| 773 0# - HOST ITEM ENTRY | |
| Title | Indian Journal of Computer Science |
| International Standard Serial Number | 2456-4133 |
| Place, publisher, and date of publication | New Delhi Associated Management Consultants |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Reference | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 05/08/2022 | 2022-1281 | 05/08/2022 | 05/08/2022 | Articles Abstract Database |